[Abstract] Fugl-Meyer Assessment Scores Are Related With Kinematic Measures in People with Chronic Hemiparesis after Stroke

Abstract

Background: Stroke often results in motor impairment and limited functional capacity. This study aimed to verify the relationship between widely used clinical scales and instrumented measurements to evaluate poststroke individuals with mild, moderate, and severe motor impairment.

Methods: This cross-sectional study included 34 participants with chronic hemiparesis after stroke. Fugl-Meyer Assessment and Modified Ashworth Scale were used to quantify upper and lower limb motor impairment and the resistance to passive movement (i.e., spasticity), respectively. Upper limb Motor performance (movement time and velocities) and movement quality (range of motion, smoothness and trunk displacement) were analyzed during a reaching forward task using an optoelectronic system (instrumented measurement). Lower limb motor performance (gait and functional mobility parameters) was assessed by using an inertial measurement unit system.

Findings: Fugl-Meyer Assessment correlated with motor performance (upper and lower limbs) and with movement quality (upper limb). Modified Ashworth scale correlated with movement quality (upper limb). Cutoff values of 9.0 cm in trunk anterior displacement and .57 m/s in gait velocity were estimated to differentiate participants with mild/moderate and severe compromise according to the Fugl-Meyer Assessment.

Conclusions: These results suggest that the Fugl-Meyer Assessment can be used to infer about motor performance and movement quality in chronic poststroke individuals with different levels of impairment.

 

via Fugl-Meyer Assessment Scores Are Related With Kinematic Measures in People with Chronic Hemiparesis after Stroke – ScienceDirect

[ARTICLE] Does the Finger-to-Nose Test measure upper limb coordination in chronic stroke? – Full Text

Abstract

Background

We aimed to kinematically validate that the time to perform the Finger-to-Nose Test (FNT) assesses coordination by determining its construct, convergent and discriminant validity.

Methods

Experimental, criterion standard study. Both clinical and experimental evaluations were done at a research facility in a rehabilitation hospital. Forty individuals (20 individuals with chronic stroke and 20 healthy, age- and gender-matched individuals) participated.. Both groups performed two blocks of 10 to-and-fro pointing movements (non-dominant/affected arm) between a sagittal target and the nose (ReachIn, ReachOut) at a self-paced speed. Time to perform the test was the main outcome. Kinematics (Optotrak, 100Hz) and clinical impairment/activity levels were evaluated. Spatiotemporal coordination was assessed with slope (IJC) and cross-correlation (LAG) between elbow and shoulder movements.

Results

Compared to controls, individuals with stroke (Fugl-Meyer Assessment, FMA-UE: 51.9 ± 13.2; Box & Blocks, BBT: 72.1 ± 26.9%) made more curved endpoint trajectories using less shoulder horizontal-abduction. For construct validity, shoulder range (β = 0.127), LAG (β = 0.855) and IJC (β = −0.191) explained 82% of FNT-time variance for ReachIn and LAG (β = 0.971) explained 94% for ReachOut in patients with stroke. In contrast, only LAG explained 62% (β = 0.790) and 79% (β = 0.889) of variance for ReachIn and ReachOut respectively in controls. For convergent validity, FNT-time correlated with FMA-UE (r = −0.67, p < 0.01), FMA-Arm (r = −0.60, p = 0.005), biceps spasticity (r = 0.39, p < 0.05) and BBT (r = −0.56, p < 0.01). A cut-off time of 10.6 s discriminated between mild and moderate-to-severe impairment (discriminant validity). Each additional second represented 42% odds increase of greater impairment.

Conclusions

For this version of the FNT, the time to perform the test showed construct, convergent and discriminant validity to measure UL coordination in stroke.

Background

Upper-limb (UL) coordination deficits are commonly observed in neurological patients (e.g., cerebellar ataxia, stroke, etc.). In healthy subjects, goal-directed movement requires synchronized interaction (coordination) between multiple effectors [1, 2, 3]. Characterizing UL coordination, however, is challenging for clinicians and researchers because of lack of consensus regarding its definition (e.g., see [4, 5, 6, 7]). Nevertheless, definitions usually describe coordinated movement as involving specific patterns of temporal (timing between joints) and spatial (joint movement pattern) variability [1, 2, 8]. However, trajectory formation differs for reaches made in a body-centered frame of reference (egocentric) compared to those relying on mapping of extrinsic space and visuo-motor transformations [9, 10] made away from the body (exocentric). Thus, coordination can be defined as the skill of adjusting temporal and spatial aspects of joint rotations according to the task [11].

Damage to descending pathways due to stroke can lead to movement deficits defined at two levels. At the end-effector level (e.g. hand), variables describe movement performance (time, straightness, smoothness, precision), whereas at the interjoint level, variables describe movement quality (joint ranges of motion, interjoint coordination) [12]. These variables may be affected differently for egocentric and exocentric movements.

Although it is widely recognized that training can improve performance of functional tasks even years after a stroke [13], a valid tool for the measurement of coordination has not yet been established. In healthy individuals, coordinated movements are described in terms of spatial variables, related to the positions of different joints or body segments in space and/or temporal variables, related to the timing between movements of joints/segments during the task [1]. Consideration of task specificity is important in characterizing coordination. In addition, movement may be affected by abnormal stereotypical UL movement synergies and concomitant reduction in kinematic redundancy [10, 14] as well as deficits reducing both movement performance and quality [15, 16].

In clinical practice, coordination is assumed to be measured by the time to perform alternating movements with different end effectors (e.g., supination/pronation of the forearm, sliding the heel up and down the anterior aspect of the shin). Another task commonly used to assess coordination is the Finger-to-Nose test (FNT) [17, 18]. In the standard neurological exam [19], the individual alternately touches their nose and the evaluator’s stationary or moving finger while lying supine, sitting or standing. In the Fugl-Meyer UL Assessment (FMA-UL) [18], the FNT is objectively measured as the difference in time to alternately touch the knee and nose five times between the more- and less-affected arm on a 0 to 2 point scale. Aside from FNT-time, two other features of endpoint performance, arm trajectory straightness/smoothness (tremor) and precision (dysmetria), are estimated qualitatively [18] for a total of six points.

However, the construct validity of FNT-time as an UL coordination measure in individuals with stroke has not been established using detailed kinematic assessment, where construct validity is defined as the degree to which experimentally-determined and theoretical definitions match [20]. For clinicians to use FNT as part of the UL assessment, this assumption must be verified along with its convergent and discriminant validity.

The study objectives were to determine construct, convergent and discriminant validity of FNT-time to measure UL coordination in individuals with chronic stroke using kinematic analysis. We characterized movement parameters during performance of FNT between healthy and stroke subjects. We also related FNT outcomes (time, trajectory straightness, precision) to UL impairment severity and activity limitations. We hypothesized that FNT-time would 1) be related to interjoint coordination measures (construct validity); 2) be correlated with other measures of UL impairment and/or activity limitations (convergent validity); and 3) discriminate between levels of UL impairment (discriminant validity). Preliminary data have appeared in abstract form [21].

Continue —> Does the Finger-to-Nose Test measure upper limb coordination in chronic stroke? | Journal of NeuroEngineering and Rehabilitation | Full Text

 

Fig. 1 a Experimental set up illustrating marker placement and examples of endpoint displacement for finger-to-nose test. Subject sat with one arm partially extended, index finger fully extended and target placed at 90% arm-length at eye-level. The task was to touch the target and then the nose accurately 10 times at a self-paced speed; b Examples of 10 trials of endpoint (tip of index finger) displacement over time. First row–healthy subject moving endpoint at self-paced speed; Second row–healthy subject moving endpoint at a slower speed and Third row–Stroke subject moving endpoint a self-paced speed

[ARTICLE] Motor Learning in Stroke – Full Text

Abstract

Background and Objective: Stroke rehabilitation assumes motor learning contributes to motor recovery, yet motor learning in stroke has received little systematic investigation. Here we aimed to illustrate that despite matching levels of performance on a task, a trained patient should not be considered equal to an untrained patient with less impairment. Methods: We examined motor learning in healthy control participants and groups of stroke survivors with mild-to-moderate or moderate-to-severe motor impairment. Participants performed a series of isometric contractions of the elbow flexors to navigate an on-screen cursor to different targets, and trained to perform this task over a 4-day period. The speed-accuracy trade-off function (SAF) was assessed for each group, controlling for differences in self-selected movement speeds between individuals. Results: The initial SAF for each group was proportional to their impairment. All groups were able to improve their performance through skill acquisition. Interestingly, training led the moderate-to-severe group to match the untrained (baseline) performance of the mild-to-moderate group, while the trained mild-to-moderate group matched the untrained (baseline) performance of the controls. Critically, this did not make the two groups equivalent; they differed in their capacity to improve beyond this matched performance level. Specifically, the trained groups had reached a plateau, while the untrained groups had not. Conclusions: Despite matching levels of performance on a task, a trained patient is not equal to an untrained patient with less impairment. This has important implications for decisions both on the focus of rehabilitation efforts for chronic stroke, as well as for returning to work and other activities.

Introduction

Stroke is a leading cause of adult disability, leaving 30% to 66% of patients with lasting motor impairment.^1,2 It has long been proposed that motor recovery following stroke is a form of relearning^3,4 and that there is considerable overlap between the brain regions involved in both processes.^5–7 However, while acquiring skill at a task may allow a patient to perform at the same level as an individual with lesser impairment, this does not necessarily make them equal. For example, well-recovered stroke patients can match the performance of healthy controls on a motor task, but differences exist in the neural networks that underlie performance for each group.⁸ Furthermore, matched performance does not necessarily imply that both groups have the same ability to continue improving given the opportunity for practice. These differences can complicate judgments regarding patients’ capacity to return to work and other activities,⁹ and which rehabilitation activities they should focus on. In this article, we propose that acquiring skill through motor training raises a similar issue—a patient who has trained on a task may “appear better,” masking categorical differences in his or her abilities. Consider two hypothetical patients—Patient A, who has mild motor impairment, and Patient B, who is more severely impaired. Patient A performs better in a movement task than Patient B. Patient B then trains at the task, reaching the same performance level as Patient A. If Patient B is now equal to Patient A, he or she should have a similar capacity for further improvement with training. If this is not the case (eg, if Patient B has reached a performance plateau beyond which further training has a limited effect), then a categorical difference remains between these patients despite their matching task performance.

In comparison to healthy individuals, stroke patients select slower voluntary movement speeds when performing movement tasks.¹⁰ As speed and accuracy are inherently linked,¹¹ a confound arises when comparing the accuracy of movements performed at different speeds. This limitation makes it difficult to interpret previous results, such as cases where patients improve their accuracy yet decrease their speed.¹² In such cases, it is impossible to determine whether a patient improved his or her ability to perform the task (through skill acquisition) or whether he or she simply changed the aspect of performance on which they focused (eg, sacrificed speed for accuracy while remaining at the same overall level of ability). The only way to disambiguate these alternatives is to first derive the speed-accuracy trade-off function (SAF¹³) for a given task; participants are required to complete the task in a fixed time, allowing accuracy to be measured without the confounding effects of differences in speed. Once derived, skill represents a shift in the SAF.^13–15

Here we introduce a serial voluntary isometric elbow force task, a modified version of the serial voluntary isometric pinch task (SVIPT). This task is based on an established laboratory-based model of motor learning in which participants learn to control a cursor by producing isometric forces.^13–19 In the task used in the present study, participants controlled a cursor by exerting forces with their elbow flexor muscles, allowing comparisons of performance across participants with greater ranges of impairment than would be possible with the standard (hand controlled) SVIPT paradigm. To control for differences in movement speeds across groups, performance was assessed by comparing the speed-accuracy trade-off pre and post training, using measures of task-level performance (ie, binary success/failure to complete all specified aspects of the task)^13–18 and trial-level measures of endpoint error and variability.²⁰ We predicted that the severity of a participant’s motor impairment would limit his or her ability to perform the task and that training may allow him or her to achieve a similar level of performance as an individual with lesser impairment. However, we hypothesized that despite their matching performance, there would be a categorical difference between these individuals; the previously untrained participant with lesser impairment would be able to make large, rapid improvements through training, while the trained participant would not.

Figure 1. Experimental setup and procedure. (A) Participants sat with their (affected) arm supported by a robotic exoskeleton. A force transducer measured contractions of their elbow flexors. (B) On screen display. Contracting the elbow flexors moved the cursor (white circle) to the right, while relaxing moved the cursor to the home position (grey square). A “go” indicator (used in training trials) indicated to participants that they could begin a trial when ready (illustrated here as a green circle). Each trial involved navigating the cursor through the sequence Home-1–Home-2–Home-3–Home-4–Home-5. Target positions and sequence order remained fixed throughout the study. (C) Procedure. Participants first completed a pretraining skill assessment, performing the task at trial durations set by an auditory metronome (indicated by tempos presented in beats per minute—see main text for further detail). One “run” of the task involved completing 10 trials at each tempo in a pseudorandom order. This procedure was repeated to generate 2 runs of data (ie, a total of 20 trials for each tempo). Participants later trained to perform the task over consecutive days, aiming to complete the sequence as quickly and as accurately as possible. Finally, on a separate day, participants completed a posttraining skill assessment.

Continue —> Motor Learning in Stroke – Oct 27, 2016

[Abstract] Motor Learning in Stroke. Trained Patients Are Not Equal to Untrained Patients With Less Impairment

Abstract

Background and Objective: Stroke rehabilitation assumes motor learning contributes to motor recovery, yet motor learning in stroke has received little systematic investigation. Here we aimed to illustrate that despite matching levels of performance on a task, a trained patient should not be considered equal to an untrained patient with less impairment.

Methods: We examined motor learning in healthy control participants and groups of stroke survivors with mild-to-moderate or moderate-to-severe motor impairment. Participants performed a series of isometric contractions of the elbow flexors to navigate an on-screen cursor to different targets, and trained to perform this task over a 4-day period. The speed-accuracy trade-off function (SAF) was assessed for each group, controlling for differences in self-selected movement speeds between individuals.

Results: The initial SAF for each group was proportional to their impairment. All groups were able to improve their performance through skill acquisition. Interestingly, training led the moderate-to-severe group to match the untrained (baseline) performance of the mild-to-moderate group, while the trained mild-to-moderate group matched the untrained (baseline) performance of the controls. Critically, this did not make the two groups equivalent; they differed in their capacity to improve beyond this matched performance level. Specifically, the trained groups had reached a plateau, while the untrained groups had not.

Conclusions: Despite matching levels of performance on a task, a trained patient is not equal to an untrained patient with less impairment. This has important implications for decisions both on the focus of rehabilitation efforts for chronic stroke, as well as for returning to work and other activities.

Source: Motor Learning in Stroke